A Data Lake is a repository

where structured and unstructured data can be stored using

multiple data

models and in multiple formats

A Data Lake is the system of software tools for processing data in

different formats

A Data Lake does not

require up-front effort to preprocess data

A Data Lake is a Data Warehouse by another name

Informing business decisions based on the patterns in data and related prognoses

Providing results that drive decisions

Answering questions on how the data needs to be processed

Processing of data to infer patterns, correlations, and models

Extracting, transforming, and loading data into a data store

A pivot table is the central node in the Star Schema representation of multidimensional data

A

pivot table is is a 2-dimensional aggregate representation of n-dimensional data where excessive dimensions are fixed for a view - as pivots

A

pivot table is a cyclic representation of the columns where the last column is made adjacent to the first - like on a cylindric surface

A

pivot table is a 2D projection of the Data Cube

ETL is the data transfer process between distributed data stores. It stands for Elicit, Transfer, and Leave Alone

ETL is the pipeline that allows logically-enabled data schema transformation. It stands for Entity Transformation Logic

ETL tasks are the pipeline that implements data extraction, cleansing, transformation, aggregation, and storing it to a Data Warehouse

ETL stands for Extract, Transform, and Load

A value could be anything in the Key-Value model, including JASON code, embedded multimedia objects, and links

Key-Value model uses a simple format for records

The Key-Value model does not support records with one key and several values

The Key-Value model is optimized for search

PUT and GET operations are fast and scale well in the Key-Value model

The values in the Key-Value model are data aggregates that may have different structures

A value within the Key-Value model is opaque and regarded as a blob

Keys in the Document-Oriented model are invisible IDs

Every document could be reached using the related search path from the root node of the document store

Document Data Model fully supports ACID transactions on documents and collections

Values in in the Document-Oriented model are white boxes having their internal structure and properties

Values in in the Document-Oriented model are indexed and are searchible by their properties

Document Data Model fully supports ACID transactions, but only on documents, not on document collections

A Document Store is organized as a tree-like structure. The nodes represent document collections, sub-collections, and individual documents

The Document-Oriented model resembles Key-Value model

Nodes and edges may have temporal bounds of their validity

Properties of data values or links are used to search matching fragments of the graph

Nodes of a graph are used to represent data objects (values)

Edges are equivalent to foreign keys in RDM

Edges of a graph are used to represent the links between data objects (values)

The Graph Data Model is equivalent to RDM in the structural aspect

Nodes are equivalent to relations in RDM

Properties are the attributes of RDM by another name

Key-Value Model provides an index structure over data aggregates

Key-Value Model is also known as a dictionary model

Key-Value Model is a different name for Hierarchical Data Model

Key-Value Model is also known as a hash model

Key-Value Model is also known as an associative array model

Key-Value Model supports sub-collections of data

Key-Value Model enables data manipulation at scale

Key-Value Model uses a graph-based structure for indexing data

Key-Value Model is good for distributing data on clusters, in a cloud

Key-Value Model copes with diversity and multi-modality of data

Key-Value Model is the same as a user view representation in the Relational Model

Column-based data model

Document-oriented data model

Key - Value data model

Relational data model

Graph data model